Sentiment analysis of Twitter data by making use of SVM, Random Forest and Decision Tree algorithm

2021 ◽  
Author(s):  
Jyotsna Singh ◽  
Pradeep Tripathi
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Twitter Data

scholarly journals CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 335-338 ◽  
Author(s):  
H. Sahu ◽  
D. Haldar ◽  
A. Danodia ◽  
S. Kumar
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Random Forest Algorithm ◽  
Synthetic Aperture Radar Data ◽  
Tree Algorithm ◽  
Maximum Likelihood Classifier ◽  
Sar Data

<p><strong>Abstract.</strong> A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47<span class="thinspace"></span>%, 0.47<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>% and 25.5<span class="thinspace"></span>% respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.</p>

scholarly journals SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

2020 ◽  
Vol 17 (2) ◽  
pp. 143-150
Author(s):  
Irwansyah Saputra ◽  
Jose Andrean Halomoan ◽  
Adam Bagusmugi Raharjo ◽  
Cyra Rezky Ananda Syavira
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Bayes Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Processing Data ◽  
Twitter Users ◽  
Using Data

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.

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